As part of a manufacturing process, a manufactured article or workpiece may be inspected for presence of defects or for compliance with a manufacturing specification or a design requirement or the like. For example, an inspection may be performed manually, such as by a human inspector with a microscope. Such manual inspection can be time- and labor-intensive. To that end, manual inspection of the workpiece can become a large part of the manufacturing process. Thus, such manual inspection may not be suitable for high-volume manufacturing processes.
Machine vision systems may be used to inspect a workpiece in order to increase inspection throughput over manual inspection by humans. Machine vision systems may analyze two-dimensional information and three-dimensional information to inspect a workpiece. As characterized by resolution, robustness, and speed of measurement, that which may be a strength of two-dimensional machine vision analysis conversely may be a weakness of three-dimensional machine vision analysis, and vice versa.
For example, two- and three-dimensional machine vision analysis of edge detection of a workpiece, such as a component made of composite tape or tow, will be considered. Regarding resolution, two-dimensional edge detection can produce an excellent average measurement of an edge line—because a large portion of an image frame is used; on the other hand, three-dimensional edge detection may be restricted to no more than the size of an image pixel and, therefore, can only locate one point. Regarding robustness, two-dimensional edge detection can be highly susceptible to image noise and surface artifacts that may resemble an edge; on the other hand, three-dimensional edge detection is not susceptible to image noise or surface artifacts at all. Regarding speed, two-dimensional edge detection may be slow—the two-dimensional edge detection algorithm must analyze an entire image before finding results; on the other hand, three-dimensional edge detection can be fast—the three-dimensional edge detection algorithm only has to analyze an image near a laser signature.
Therefore, a two-dimensional machine vision edge detection algorithm may perform well at determining precise location of an edge. However, the two-dimensional machine vision edge detection algorithm may be fooled by image noise and surface artifacts into finding “edges” that are not there. Moreover, analysis of an entire image by the two-dimensional machine vision edge detection algorithm may be time consuming. On the other hand, a three-dimensional machine vision algorithm can quickly detect a point without being susceptible to image noise or surface artifacts. However, the three-dimensional machine vision algorithm can only locate one point that is no more than the size of the image pixel.
Thus, neither two-dimensional machine vision analysis nor three-dimensional machine vision analysis is superior to the other in all three characteristics of resolution, robustness, and speed. However, two-dimensional machine vision analysis and three-dimensional machine vision analysis may be complementary to each other in the characteristics of resolution, robustness, and speed.
The foregoing examples of related art and limitations associated therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the drawings.